def __init__(self, inferenceType=InferenceType.TemporalNextStep, encoderParams=()): super(TwoGramModel, self).__init__(inferenceType) self._logger = opf_utils.initLogger(self) self._reset = False self._hashToValueDict = dict() self._learningEnabled = True self._encoder = encoders.MultiEncoder(encoderParams) self._fieldNames = self._encoder.getScalarNames() self._prevValues = [] * len(self._fieldNames) self._twoGramDicts = [dict() for _ in xrange(len(self._fieldNames))]
def __init__(self, inferenceType=InferenceType.TemporalNextStep, encoderParams=()): """ Two-gram model constructor. inferenceType: An opf_utils.InferenceType value that specifies what type of inference (i.e. TemporalNextStep, Classification, etc.) encoders: Sequence of encoder params dictionaries. """ super(TwoGramModel, self).__init__(inferenceType) self._logger = opf_utils.initLogger(self) self._reset = False self._hashToValueDict = dict() self._learningEnabled = True self._encoder = encoders.MultiEncoder(encoderParams) self._fieldNames = self._encoder.getScalarNames() self._prevValues = [None] * len(self._fieldNames) self._twoGramDicts = [dict() for _ in xrange(len(self._fieldNames))]